Soft computing techniques have the ability to handle complex, uncertain, and imprecise information to create usable solutions to convoluted problems or those just too time-consuming to solve with current hardware. This new book details the use and applications of soft computing technology in several fields. Applied Soft Computing Techniques: Theoretical Principles and Practical Applications is strategically arranged into five sections to explore the use of these techniques in biomedical applications, communication technologies, data analytics and applications, image processing, and natural language processing.
The chapters in the section on biomedical applications explore soft computing techniques for cancer data analysis, depression and mental health analysis, heart disease detection, etc. The editors go on to discuss soft computing in communication systems, looking at graphs, design processes, and mapping techniques, as well as the integration of IoT devices, drone technology, etc. The volume also details how soft computing methodologies can assist in tackling the obstacles associated with signal processing, network optimization, quality of service, and beyond.
Several chapters discuss the use of soft computing techniques in data compression, handling of large-scaled heterogenous databases, visualization techniques, etc. Applications of soft computing in image processing are also discussed and cover human face recognition, casualty detection, traffic sign recognition, and predicting soil features using satellite imagery. Soft computing techniques in natural language processing consider text-to-speech signal conversion, NLP and speech recognition, speech emotion recognition, and more.
This volume will help to facilitate the amalgamation of theoretical principles and practical applications, bringing forth possible solutions to complex problems in various domains. The book is a welcome resource for researchers, students, professionals, and even for individuals looking for knowledge on soft computing.
Applied Soft Computing Techniques: Theoretical Principles and Practical Applications
will help to facilitate the amalgamation of theoretical principles and practical applications, bringing forth possible solutions to complex problems in various domains. The book is a welcome resource for researchers, students, professionals, and even for individuals looking for knowledge on soft computing.
Discusses use of applied soft computing techniques in biomedical applications, communication technologies, data analytics and applications, image processing, and natural language processing. Details how soft computing can assist in tackling obstacles associated with signal processing, network optimization, quality of service, and beyond.
Introduction PART I: BIOMEDICAL APPLICATIONS
1. Soft Computing
Algorithms and Their Applications in Breast Cancer Data Classification: An
Experimental Analysis
2. Real-Time-Based Heart Patient Monitoring System: An
Application of Health Care IoT
3. Sentiment and Depression Analysis Using
Machine Learning
4. Brain Tumor Detection Using Deep Convolution Neural
Networks
5. Smart Healthcare Approach Using Machine Learning for Breast
Cancer Diagnosis and Prediction
6. Sedona: A Mental Health Tracker
7. Machine
Learning-Based Heart Disease Prediction System
8. Breast Cancer Risk
Detection PART II: COMMUNICATION TECHNOLOGIES
9. A Comprehensive Study on
Graphs, Design Process, and Mapping Techniques based on Evolutionary
Algorithms for Network on Chip Architecture
10. Integrating IoT Devices to
Renewable Energy Systems for Increasing Energy Efficiency
11. Architectures
of Computer Networks with Standard
12. Drone-Enabled Precision Agriculture:
Revolutionizing Crop Management and Yield Optimization PART III: DATA
ANALYTICS AND APPLICATIONS
13. Machine Learning-Based Intrusion Detection
Techniques: A Concise Study
14. Data Compression for Achieving Cost-Efficient
and Secure Data Storage over Public Cloud: A Proposed Model
15. Accessibility
of Data Linked to Large Scale Heterogeneous Databases Using Generalized
Approach: A Soft Computing-Based Approach
16. Visualization and Comparative
Simulation of Pathfinding, Searching, and Sorting Algorithms
17. Educational
Website with Search Engine Optimization PART IV: IMAGE PROCESSING
18. A
Comparative Analysis on Support Vector Machine, k-Nearest Neighbors, Naive
Bayes, and Decision Tree Classifiers Applied for Human Face Recognition
19.
Study and Analysis of Computer Vision for Casualty Detection
20. Traffic Sign
Recognition Using a Convolutional Neural Network
21. An Optimized Soil
Features Prediction Using a Satellite Image Database PART V: NATURAL LANGUAGE
PROCESSING
22. A Novel Technique Enabling Text-to-Speech Signal Converting
System Using Raspberry Pi
23. Self-Learning Artificial Intelligence Smart Bot
with NLP and Speech Recognition
24. Application of Multilayer Perceptron in
Speech Emotion Recognition
25. Bangla and Odia Machine Translation Using EM
Algorithm: Experimental Evaluation
Samarjeet Borah, PhD is Professor and Head in the Department of Computer Applications at SMIT, Sikkim Manipal University, India. He has carried out research projects, published research papers in journals and conferences, and organized workshops and conferences. He is Editor-in-Chief of the book series Research Notes on Computing and Communication Sciences. Dr. Borah has served as a keynote as well as invited speaker at several international conferences. His areas of research are data mining, information security, data science, and machine learning.
Ratna Raja Kumar Jambi, PhD is Principal at the Genba Sopanrao Moze College of Engineering, Balewadi, Pune, India. He has 17 years of teaching and research work. He has patents at both national and international levels and has published papers and book chapters on artificial intelligence and machine learning. He has organized national and international conferences. He received an Innovative Leader Award from the World Education Summit & Awards in 2019 in New Delhi.
Sharifah Sakinah Syed Ahmad, PhD is currently working as Associate Professor in the Department of Intelligent Computing & Analytics (ICA), Faculty of Information & Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM). Her current research work is on the granular fuzzy rule-based system, evolutionary method, modeling, and data science. Dr. Ahmad has been a member of numerous program committees at national and international conferences in intelligent systems and soft computing, where she is involved in organizing and reviewing activities. She is an editorial board member of the IEEE Transactions on Systems, Man, and Cybernetics: Systems and a member of the Association for Computing Machinery (ACM). She has co-authored numerous journal publications, conference articles, and book chapters. She has also received several grants from various funding agencies, including the Ministry of Education, Malaysia, and the Ministry of Science, Technology, and Innovation, Malaysia. Dr. Ahmad has 15 years of experience in teaching. She was also involved in conducting several workshops related to improving data science and modeling.
Mahendra Prabhakar Deore, PhD is an Assistant Professor of Computer Engineering at MKSSSs Cummins College of Engineering for Women, Pune, India. With over 16 years of dedicated teaching experience at undergraduate level, Dr. Deore has authored and published more than 20 SCI- and SCOPUS-indexed research papers. His primary areas of interest encompass big data, security, computer networks, and machine learning.
